“Logistics Made in Germany” – Round Table Discussion

Organized jointly by “Logistics alliance Germany (LGA) ” and “CII Institute of Logistics”, this thought leadership round table discussion focused on how LGA can help Indian companies find competent logistics partners in Germany to access the European market or to co-operate with.

The delegates attended the round table discussion included Dr. VeitSteinle, Director, and Federal Ministry of Transport, Building and Urban Development, Mr.Peter Luttjohann from Federal Ministry of Transport, Building and Urban Development, Mr. Michael Kuchenbecker from Logistics Alliance Germany and Mr. Andreas Weber from Logistics Alliance Germany.

This event was chaired by our CEO & Director, Bala. Padmakumar

The objective of the round table is to understand better the needs and requirements of Indian companies exporting to Europe or planning to export to Europe, and to provide political support, identifying and contacting interested Indian partners, and to start an exchange of information and to detail the company-specific requirements.

Participants gathered insight into
• Information about Germany as a gateway and hub to cover the European market.
• Information about the Logistics Alliance Germany and its services.
• Information about operational support in terms of logistics services provided by logistics companies in Germany.
• Information overview on the German Freight Transport and Logistics Action Plan.

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Risk Management in Financial Institutions Recent advances

“Risk Management in Financial Institutions – Recent advances” presentation made by Padmakumar. Bala, CEO OptiRisk, at the “International Conference on Mathematics in Engineering & Business Management” on 10th March 2012 organized by Loyola collage, Chennai. The presentation is available here

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Managing uncertainty of Asset and Liability world: Liability driven investment (LDI)

Typically, as a young professional, we set up savings for our children’s future. These savings are assets suitably invested in bonds, shares, gold, and real estate. The liabilities are usually future payment for college fees, and marriage. So, we can say that all of us are exposed to a bit of simple Asset Liability Management (ALM) in our lives.

ALM, as the name suggests, is the management of assets and liabilities in coordinated way; it is important for both the common man and the big corporate houses. In our personal lives, we would be managing small cash inflows and outflows, which do not include a lot of planning, uncertainties and government regulations. But what happens with big corporations like banks and insurance companies is quite different.

The Insurance Provider:

Let’s assume you are the owner of an insurance company and you are in the business of simple annuity product in India. The generic features of your product are:

  • The policy buyer is supposed to pay a fixed amount once while buying the Annuity Policy.
  • The policy holder will get the policy payout every six months on the agreed terms.
  • Policy maturity period is till the death of the policy holder. When the policyholder dies, the nominee receives the ‘sum assured’ (equal to Annuity premium value) and the last coupon payment.

For simplicity, let’s say, initially you sold just one Annuity policy in the market of sum amount ‘X’ and you are paying the biyearly coupon to the annuitant at the rate of say ‘A %’. Immediately invested the same amount ’X’ in twenty-year maturity government bonds. You bought the government bond because it has less credit risk or chance of default. The bond that you bought is paying you the coupon every six months at some fixed pay-out ‘B%’ ,which we assume is more than ‘A %’ implying that your outflow coupon value is less than the inflow coupon value. Hence, we can conclude that in favour of managing the two cash flows; one from the bond and the other from the Annuity Policy, you are earning the difference between the two coupon values.

Uncertainty of the Asset and Liability world:

In the above case, liability is the biyearly coupon value and the sum assured on the death of the policy holder.  The latter can happen any time so you must have the money ready with you every time to pay the liabilities; this is called the Liquidity Problem. On the death of the annuitant, you need to sell your portfolio (in this case the government bond you bought) to pay the `sum assured’ to the nominee.

The asset is the twenty-year maturity government bond. The coupon money you get from your bond does not change till the maturity of the bond, but the market value of your bond changes as the interest rate changes in the market; it may go up or down. Due to this, you face another risk that is called `Interest Rate Risk’. If the interest rate values go down, then your bond market value increases and your profit goes high, but if it goes up, your bond value will go down and you may have to face the crisis.

Asset and Liability Management Problem:

Above was a simple example of how the cash flow works in the insurance sector. The primary issue with ALM or cash flow matching is the duration of your portfolio or bonds, and the Annuity policy.

The big Insurance companies who have thousands of bonds, stock, other assets in their portfolio and thousands of sold Annuity policies will have a more complex ALM problem. Also, the kind of risk they are bearing depends upon the asset classes i.e. bonds, stocks, equities, derivatives, and cash etc.

In addition to the different risks, the biggest single factor explaining performance of your portfolio is simply the asset allocation decision that determined how much a fund should hold in stocks, bonds or cash etc.

Over and above all these, companies have one common goal – to maximize their wealth.

From a mathematical perspective, this complex ALM problem can be set up in an equation form involving non-negative variables which represent inflow and outflow of funds and carry-over of retained assets and funds from one planning period to the next.

Liability Driven Investment: A potential solution considering uncertainty

There are a number of established techniques to consider the ALM problem mentioned above. In simple words, LDI is a technique to allocate assets while keeping the liabilities in mind. So that finally you are solving your cash flow matching problem by buying a portfolio that will give you an inflow that matches or surpasses your outflow.  That only happens in the deterministic world because in the real world, the inflow from an asset and the outflow from liabilities are not known beforehand. You can just predict some future value called scenario.

The robust and stochastic formulation of LDI, considers the different scenarios of the liability and assets i.e. different values of mortality rate (death date) of the policy holders (Longevity risk),different values for the interest rate (Interest rate risk), equities, stocks and market indices (Market risk) at each cash flow balancing step of the planning horizon.

Hence, we can say that to avoid the financial quagmire, requires advanced and meticulous financial planning, and for large organisations LDI is invaluable.

We will look into further details of LDIOpt in the coming weeks…

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Investment Planning

Al Ferris has $60,000 now.  He wishes to invest this amount in risk free fixed income securities (Government Bonds or Fixed Deposits) in order to use the accumulation for purchasing a retirement annuity at the end of 5 years.  After consulting with his financial adviser, he has been offered four types of fixed-income investments, which we will label as investments A, B, C and D.

Investments A and B are available at the beginning of each of the next 5 years (call them years 1 to 5).  Each dollar invested in A at the beginning of a year returns $1.40 (a profit of $0.40) 2 years later (in time for immediate reinvestment).  Each dollar invested in B at the beginning of a year returns $1.70 three years later.

Investments C and D will each be available at one time in the future.  Each dollar invested in C at the beginning of year 2 returns $1.90 at the end of year 5.  Each dollar invested in D at the beginning of year 5 returns $1.30 at the end of year 5.

Al wishes to know which investment plan maximizes the amount of money that can be accumulated by the beginning of year 6.

(Problem taken from “Introduction to Operations Research” by Hillier/Lieberman)


An excel solution for this problem involving 9 decision variables can be found here.

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Data Analytics – the next big thing in India

In the recent years, increasing number of companies are making analytics and Business Intelligence their top priority.  A 2009 IBM survey showed that 83% of CIO’s believe Business Intelligence is the single largest source of competitive advantage for their companies.

Bureau of Labor studies in USA believes that Management Analytics jobs will increase by 24% between 2008-2018, much faster than that of average job growth for the rest of the economy.

The word ‘analytics’ is being searched more frequently across India every single year since 2009.
In US,  average salaries for analytics workers went up by 10% in the last one year.

‘Analytics’ means the extensive use of data, statistical and quantitative analysis, exploratory and predictive models and fact based management to drive decisions and actions.  Such analytics may be the input for human decisions or may fully drive automated decisions.

Why Predictive Analytics is a must in today’s business environment?

  1. It can help to change the game in an industry quite profoundly
  2. Outperform your competitors
  3. Sell (new) products/services based on analytics
  4. Identifying profitable and loyal customers, and charging them the optimal price
  5. Maintaining lowest possible level of inventory while avoiding out-of-stock
  6. Hiring, retaining and promoting the best people in the industry
  7. Choosing the best location for your stores
  8. Selecting the best candidates for mergers and acquisitions
  9. Optimizing supply chains and delivery times

10.  Identifying customer preferences

Predictive Marketing

  • Helps companies like ING, Vodafone and ABN Amro to significantly improve their direct marketing activities by enabling them to predict the preferences and needs of their individual customers and base the marketing campaigns on these needs.
  • These companies, reduced their costs of marketing activities by 15% to 30%, they doubled the response rates and generated 25% to 50% more profit out of their marketing actions.

Predictive Analytics in Call Center

  • Helps companies like Aegon and ABN Amro to effectively turn their service call centers into profit centers by automatically alerting call center agents on hidden product needs and retention risks for the current caller and advise the most appropriate offer and treatment during the call.
  • A very high accuracy is achieved by analyzing the current call and combine this with historic and other data on the specific customer.
  • In one case this system was able to turn a service call center into a profit center, generating $30 million additional sales per year.

Predictive Claims

  • Has helped organizations like ING, Banco Commercial and many others to improve their claim handling processes, and enabled them to:
    • Identify which customers can be trusted and focus on excellent service to these customers
    • Reduce claim handling costs by 20% to 40% by selecting low risk claims for fast tracking
    • Discover twice or three times as much fraud by identifying the highest risk claims

Cablecom, a Swiss-based telecommunications provider, leveraged IBM SPSS predictive analytics tools and succeeded in reducing customer churn from 19% to 2%. The telecommunications company surveyed customers at critical interaction points, used the survey results to build models that predicted satisfaction levels, and created and executed campaigns aimed at retaining at-risk customers’

Case History of a buyer who regularly visits Shoppers Stop

I recently went with my friends to Shoppers Stop store at Inorbit Mall, Mumbai. I am a regular consumer of their products. I was pleasantly surprised that the footwear section has been moved to the ground floor. Previously, it was on the first floor. Now it is right next to the clothes & garments section. When I asked about the change, said the salesman out there, that it is the result of an adjacency analysis Shopper’s Stop had performed. After conducting analysis on 24 months of customer data, they found that consumer who buy the ethnic wear, also buy foot wear.  Based on this finding, they changed the  foot wear section to the ground floor next to the garments section.  After the change of location, the sales of foot wear went up by 25%.

Following are some of the Indian Companies, which are currently using Analytics for Management Decision Making.

Indian Banks with Analytics operations

  • ICICI, Mumbai
  • HDFC, Mumbai

Mobile Service provider with Analytics Operations

  • Vodafone Telecommunications, Chennai, Mumbai
  • Nokia Networks, Gurgaon
  • Airtel, Gurgaon

Indian companies with Advanced web analytics

  • Rediff.com
  • Bharatmatrimony.com (Consim info pvt ltd.), Chennai
  • Naukri.com (Info edge group), Chennai
  • Timesofindia (Online Newspaper),
  • Irevena, Chennai
  • Amba Research, Bangalore

Indian Retail stores within house Analytics operations

  • Shopperstop
  • Reliance retail.

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Facility Layout Planning Using EXCEL

Russel has a machine shop on Downing Street. He is planning on expanding his workshop to meet the increasing demands of his customers. Currently there are four work centers in his shop. He feels that an expected increase demand in the near future might be satisfactorily met by equipping his machine shop with four new work centers. The diagram below gives a schematic layout of a machine shop with its existing work centers designated by squares 1, 2, 3 and 4. Four new work centers I, II, III, and IV are to be added to the shop at locations designated by circles a, b, c, and d. The objective is to assign the new centers to the proposed locations to minimize the total materials handling traffic between the existing centers and the proposed ones. Table below summarizes the frequency of trips between the new centers and the old ones. Materials handling equipment travels along the rectangular aisles intersecting at the locations of the centers.

Frequency of trips

New Center
1 10 2 4 3
2 7 1 9 5
3 0 8 6 2
4 11 4 0 7

The situation can be modeled using mathematical programming and solved using excel. The EXCEL solution can be downloaded here.

Financial Modelling With Excel

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Nova Backpack Inventory Planning

Nova manufactures backpacks for serious hikers. The demand for its product occurs during March to June of each year. Nova estimates the demand for the four months to be 100, 200, 180, and 300 units, respectively. The company uses part-time labor to manufacture the backpacks and, accordingly, its production capacity varies monthly. It is estimated that Nova can produce 50, 180, 280, and 270 units in March through June. Because the production capacity and demand for the different months do not match, a current month’s demand may be satisfied in one of three ways.
1. Current month’s production.
2. Surplus production in an earlier month.
3. Surplus production in a later month (backordering).
In the first case, the production cost per backpack is $40. The second case incurs an additional holding cost of $.50 per backpack per month. In the third case, an additional penalty cost of $2.00 per backpack is incurred for each month delay.

The optimum production schedule for the months can be determined.

Download the solution files here.

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Accent Oil Production Schedule

Accent Oil, located on the Island of Easter, has a capacity of 1,500,000 bbl of crude oil per day. The final products from the refinery include three types of unleaded gasoline with different octane numbers (ON): regular with ON = 87, premium with ON = 89, and super with ON = 92. The refining process encompasses three stages:
(1) a distillation tower that produces feedstock (ON = 82) at the rate of .2 bbl per bbl of crude oil,
(2) a cracker unit that produces gasoline stock (ON = 98) by using a portion of the feedstock produced from the distillation tower at the rate of .5 bbl per bbl of feedstock, and
(3) a blender unit that blends the gasoline stock from the cracker unit and the feedstock from the distillation tower.
The company estimates the net profit per barrel of the three types of gasoline to be $6.70, $7.20, and $8.10, respectively. The input capacity of the cracker unit is 200,000 barrels of feedstock a day. The demand limits for regular, premium, and super gasoline are 50,000, 30,000, and 40,000 barrels per day.

A model for determining the optimum production schedule for the refinery can be developed.

Download the solution files here.

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How to make better decisions in business?

Scientific methods can be used to plan, schedule, manage risk and improve productivity. It has been proved that by using scientific methods, businesses have reduced cost and investment, have improved revenue, resulting in better ROI.

Almost all business problems have many possible solutions; but the challenge is to pick the BEST solution that consumes the LEAST resource. To solve such problems just by mere  gut feeling alone could result in wasted resources and cost.

The following example shows how one could save cost in a simple warehouse lease planning situation by applying scientific methods.

Ace Hardware sells household hardware products through an organized marketing campaign on FaceBook. It needs substantial warehouse space for storing its merchandize. The management is coming up with a plan to lease space for storage for the next six months. The space requirement for each month is as follows:


space required

(square feet)

1 33,000
2 17,000
3 10,000
4 20,000
5 40,000
6 65,000

Discount is available for longer lease duration, as shown below. So, it is reasonable to lease the maximum needed warehouse space for the entire period of 6 months.
A lease is signed for a minimum period of one month. The cost to lease the warehouse is as follows:

Leasing Period Cost per square feet Leased (Rs)
1 30
2 50
3 70
4 85
5 100
6 110

The objective is to minimize the total cost incurred for leasing the space for six months and at the same time meeting the space requirements.

Many decision makers follow the brute force method, where they lease the minimal space requirement (10,000 sq.ft – month #3 requirement) for 6 months duration and then lease the additional requirements on a monthly basis.

Brute force method may not be the best decision, as it may  result in increased and unnecessary expenses.

This problem can be solved using scientific methods (aka Operation Research Models) to identify the best leasing plan for the given requirements.

The following presentation has the details of how to solve the problem using EXCEL.

Download the EXCEL file along with the solution here.

The same methodology can be extended to solve multi-location warehouse leasing problems.

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Is your investment in IT aligned with your business objectives?

Nowadays, we hear news about the revival of world economy. In India, though we have world class, matured quality processes/standards like CMM, ISO standards, six sigma etc., along with best solution approaches/models,there are many businesses which struggle for survival as their quarter to quarter bottom line keeps moving down with increased cost and ROI (Return on Investments) in remote corner. And, there is no sign of coming closer to profitability. There could be various reasons for such downfall. One of the reasons could be the non-alignment of IT (Information Technology) with business objectives. Such non-alignment could negatively impact the organizational performance and hamper achieving business objectives / goals / strategies.

Alignment of IT with Business:

IT is for business, and not business for IT. Let us consider the HR department of XYZ co. for our analysis. The HR department can be further divided into sub-departments like Administration, Recruitment, Personnel, Training and Pay roll.

No Department


Sub Dept. Name Name of the Application/


Has the application covered the entire business process? What is the %of coverage/ alignment of IT with Business Gap in %
1 Human Resource(HR) Administration No application Not applicable 0 Not applicable
2 -do- Recruitment No application Not applicable 0 Not applicable
3 -do- Personnel Microsoft No 65 35
4 -do- Training Java No 72 28
5 -do- Pay roll SAP- Pay roll Yes 100 0

Please look at the above table. Of the five sub-departments, the IT investment in pay roll department is 100% aligned with business requirements. IT investment in Personnel and Training sub-departments are 65% and 72% respectively. There is a gap between IT investment and Business requirements. The meaning here is all the processes of the particular sub-department are not coming under IT/not covered by computerization.

It is a clear indication that even though the said organization has invested in IT, it is not strategically aligned with business objectives. In the above scenario, how can one expect increasing bottom line for the said business? If we analyze further, we can find out there could be various reasons for the non- alignment. One of the reasons could be lack of proper planning, lack of knowledge/understanding about functional domain areas, less/no knowledge on IT systems and it’s alignment with business objectives. The remedial measure would be an analytical study on IT Portfolio Rationalization. Result of IT Portfolio Rationalization study would give the recommendations on 4Rs+ framework as below.

  • Retain (Minimal Changes, Superior applications/projects, High Business value)
  • Repair (Upgrades, Enhancements, Migrations for improvements and consolidations)
  • Re-engineer (Benchmark, Find Best-fit solutions)
  • Retire (Sunset, Decommission)

Business Solutions Framework (BSF), developed by Wilfred, can be used to come up with the road map of IT software applications / projects with milestones as per the 4Rs decision framework for the categories: Repair, Re-engineer, Integration and Outsourcing of the applications/projects. This road map throws more light on various business process scenarios with appropriate IT implementations that can be modeled and simulated using Business Process Management tools.  Six Sigma tools and techniques could be used to measure and improve the process capabilities to achieve better ROI

Business Solutions Framework

Business Solutions Framework

Fig-1: Business Solutions Framework

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